Channel integrity detection and reconstruction of electrophysiological signals

ABSTRACT

This disclosure relates to integrated channel integrity detection and to reconstruction of electrophysiological signals. An example system includes a plurality of input channels configured to receive respective electrical signals from a set of electrodes. An amplifier stage includes a plurality of differential amplifiers, each of the differential amplifiers being configured to provide an amplifier output signal based on a difference between a respective pair of the electrical signals. Channel detection logic is configured to provide channel data indicating an acceptability of each of the plurality of input channels based on an analysis of a common mode rejection of the amplifier output signals.

TECHNICAL FIELD

This disclosure relates to integrated channel integrity detection and toreconstruction of electrophysiological signals.

BACKGROUND

Body surface electrical activity (e.g., electrophysiological signals)can be sensed by an arrangement of electrodes. The sensed signals can beprocessed for a variety of applications, such as for body surfacemapping or reconstruction of onto a surface such as forelectrocardiographic mapping. Since these and other processing methodscan depend on body surface potential data, the presence or absence ofquality signals can affect outputs generated based on signal processing.

SUMMARY

In one example, a system includes a plurality of input channelsconfigured to receive respective electrical signals from a set ofelectrodes. An amplifier stage includes a plurality of differentialamplifiers, each of the differential amplifiers being configured toprovide an amplifier output signal based on a difference between arespective pair of the electrical signals. Channel detection logic isconfigured to provide channel data indicating an acceptability of eachof the plurality of input channels based on an analysis of a common moderejection of the amplifier output signals.

In another example, a method includes receiving, via a plurality ofinput channels, respective input electrical signals sensed by a set ofelectrodes. The method also includes amplifying, via a plurality ofdifferential amplifiers, a difference between respective pairs of theinput electrical signals and providing an amplified output signalcorresponding to the difference. The method also includes analyzing theamplified output signals to determine a relative impedance associatedwith each electrode in the set of electrodes. The method also includesgenerating channel data to specify an acceptability or unacceptabilityfor each of the plurality of input channels based on the analyzing.

As another example, a system includes a plurality of electrodesconfigured to sense electrical signals across a body surface of apatient. A processor executes machine readable instructions stored inone or more non-transitory media. The instructions are configured tocompute a transformation matrix based on at least one boundary conditionand geometry data associated with the plurality of electrodes. Theinstructions are further configured to modify the transformation matrixbased on bad channel data specifying that one or more of a plurality ofinput channels, which receive electrical signals from the plurality ofelectrodes, are unacceptable while retaining geometry information foreach of the plurality of electrodes. Reconstructed electrical signalsare estimated on a cardiac envelope based on the modified transformationmatrix and the electrical signals from the plurality of electrodes

As another example, a method includes storing geometry data andelectrical signal data associated with a plurality of electrodesarranged for sensing body surface electrical signals. The method alsoincludes computing a transformation matrix based on at least oneboundary condition and geometry data associated with the electrodes. Themethod also includes modifying the transformation matrix based on badchannel data specifying that a connection of one or more of a pluralityof electrodes with the body surface is unacceptable while retaininglocation information for each of the plurality of channels and providinga modified transformation matrix. The method also includes estimatingthe reconstructed electrical signals on the cardiac envelope based onthe modified transformation matrix and the electrical signals from theplurality of electrodes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a system to determine integrity of inputchannels.

FIG. 2 depicts a block diagram of part of the system of FIG. 1 forprocessing a pair of input channels exhibiting a first common moderejection performance.

FIG. 3 depicts a block diagram of part of the system of FIG. 1 forprocessing a pair of input channels exhibiting a second common moderejection performance.

FIG. 4 depicts an example of another system for determining integrityfor a plurality of electrodes.

FIG. 5 is a block diagram depicting an example of channel detectionlogic.

FIG. 6 is an example of waveforms in the example system of FIG. 5.

FIG. 7 is an example of other waveforms in the example system of FIG. 5.

FIGS. 8 and 9 depict examples of a set of electrodes divided into zones.

FIG. 10 is a flow diagram depicting an example of method for determiningan acceptability or unacceptability for a plurality of input channels.

FIG. 11 depicts an example system to generate reconstructed electricalsignals.

FIG. 12 is a flow diagram depicting an example of a method forgenerating reconstructed electrical signals.

FIG. 13 depicts an example of an adjusted transformation matrix.

FIGS. 14A and 14B depict examples of graphical maps generated fromelectrical signals for a normal sinus rhythm reconstructed with andwithout including signal information for bad channels.

FIGS. 15A and 15B depict examples of graphical maps electrical signalsfor a premature ventricular contraction reconstructed with and withoutincluding signal information for bad channels.

FIG. 16 depicts an example of a system for generating graphical outputsbased on electrophysiological signals measured from a patient's body.

DETAILED DESCRIPTION

This disclosure relates to systems and methods to determine channelintegrity for a plurality of input channels. Each of the input channelscan carry sensed electrical signals from a respective electrode.Channels identified as being unacceptable (or not classified as beingacceptable) may be utilized in further processing and analysis. As anexample, the further processing and analysis can include reconstructingsignals on a body surface based upon the input channel data (e.g., viaan inverse solution). Additional calculations can be performed on thereconstructed data, such as to generate one or more graphical maps andcharacterize the reconstructed data.

As an example, the channel integrity systems and methods may beimplemented to provide channel data that identifies which channels mayinclude signal outside of expected operating parameters, such as due toelectrodes failing to contact a target or otherwise fail to establishacceptable contact with the target. For example, differential amplifiersare configured to provide amplifier output signals based on a differencebetween respective pairs of the electrical input signals. The signalsmay be filtered so as to include a common signal of the system, such asa line interference (noise) signal. The amplified signals are furtherprocessed to analyze the amplifier output signals to ascertain a commonmode rejection of the electrodes. A relative impedance of the inputchannels may be implied from the common mode rejection performance ofthe input channels, such as by detecting that a given channel has ahigher voltage than one or more other channels. The relative impedancemay be utilized to derive the channel data specifying an acceptabilityof contact between each electrode and the target. Unlike some existingapproaches, the systems and methods disclosed herein can specify bothwhich electrodes are in contact with the target and, among thoseelectrodes that are in contact, which electrodes are not establishing anacceptable contact. In some systems, little or no modifications toexisting hardware. By removing bad or missing channels from furtherprocessing, the approach can not only achieve improved accuracy in suchfurther processing and analysis but also improves the system's workflow,such as by reducing preprocessing time.

This disclosure also relates to systems and methods to reconstructelectrical activity on a surface of interest within a patient's bodybased on signals measured on an outer surface of the patient's body(e.g., via electrodes). For example, systems and methods are disclosedto implement an inverse method calculation by reconstructing electricalsignals on a surface (e.g., cardiac envelope) within a patient's bodybased on the measured electrical signals and geometry data representinggeometry of the set of electrodes relative to anatomy (e.g., inthree-dimensional space). The inverse reconstruction may includecalculating a transformation matrix based on at least one boundarycondition and the geometry data. The boundary condition can varydepending on the inverse reconstruction method being implemented. Anychannel for which an electrode does not adequately contact the target isidentified and stored in channel data. The channel data may be generatedaccording to any channel integrity methods disclosed herein, manualmethods as well as other approaches. The transformation matrix that isused in the inverse reconstruction is adjusted based on the channel datato provide a modified transformation matrix. As one example, this mayinclude removing electrical signal data for each bad channel from thetransformation matrix while still retaining geometry information for allchannels including bad channels. As another example, the adjustment mayinclude replacing the electrical signal information for each bad channelin the transformation matrix with unknown variables for such channels.The modified transformation matrix thus may be employed with the inputelectrical signals to compute the reconstructed electrical signals onthe cardiac envelope. The systems and methods disclosed herein forreconstructing the electrical signals on the surface thus may achieveimproved accuracy over other approaches (e.g., that use interpolatedsignals for channels).

FIG. 1 depicts an example of a channel integrity detection system 100that can be utilized to provide an indication of channel integrity for aplurality of input channels. For example, the channel integrity mayindicate whether physical contact between an electrode, which provides asensor signal, and a target is acceptable or unacceptable (e.g., amissing or bad connection) for further processing of the sensor signal.The channel integrity system 100 can be implemented as hardware (e.g.,circuitry and/or devices), software (e.g., a non-transitory mediumhaving machine readable instructions) or a combination of hardware andsoftware.

The system 100 includes a plurality of N input channels 102 configuredto receive respective electrical signals from a set of electrodes, whereN is a positive integer greater than two. In some examples, the inputchannels 102 provide electrical signals sensed by sensing electrodesthat are placed on a body surface of the patient, which can be aninternal body surface (e.g., invasive) or an external body surface(e.g., non-invasive) or a combination thereof. In many examples herein,the body surface is described as the patient's thorax, such as forsensing cardiac electrical activity. In other examples, other bodysurfaces may be used, such as the head or other parts of the bodyaccording to the purpose for which electrical activity is being sensed.In some examples, the input channels can correspond to pre-filteredinput data, such as prior to implementing line-filtering and othersignal processing (e.g., offset correction, analog-to-digital conversionand the like) to remove selected noise components from the respectiveinput channels. Each of the input channels 102 may thus include powerline interference signals, corresponding to a common mode signal foreach channel and the system 100.

The input channels 102 provide respective electrical signals to anamplifier stage 106. In some examples, a filter 104 is coupled betweeneach input channel and a respective input amplifier stage. For instance,each filter 104 is configured (e.g., as a low pass, anti-aliasingfilter) that attenuates or blocks frequencies higher than apredetermined cutoff frequency. Each filter 104 provides a filteredsignal having a frequency below the cutoff frequency such that thefiltered signal includes a common mode signal. The filters 104 arecoupled to provide their filtered signals to one or more inputs of theamplifier stage 106. By utilizing the line noise signal as a common modesignal for the system 100, no additional input signals need to beinjected into the system to detect channel integrity, as disclosedherein.

The amplifier stage 106 includes a plurality of differential amplifiers108, each configured to provide an amplified output signal based on adifference between a respective pair of input electrical signals fromrespective input channels. For example, each respective pair of inputchannels may be connected to inputs of one or more differentialamplifiers. In an example, the filtered signal may be directly connectedto the inputs of the differential amplifiers. In another example thefiltered outputs may be connected to the inputs of the differentialamplifiers via other circuitry (e.g., a switching network—not shown)that routes the filtered input channel signals to the amplifier inputs.In some examples, a switching network may be used to selectively connectthe filters 104 into the channel paths (between the input channels andamplifier stage) for performing channel integrity functions and out ofthe channel paths for implementing other signal processing functions.

The system also includes channel detection logic 110 configured toprovide channel data 112 indicating an acceptability or unacceptabilityof each of the plurality of input channels. As disclosed herein, thechannel detection logic 110 can analyze a common mode rejectionperformance based on the amplifier output signals. By way of example,since the electrodes for each channel are almost identical, a commonrange for electrode impedances are expected assuming connection qualityfor each electrode to the target is proper (e.g., good electricalcontact between the electrode and the target). Therefore, a common modesignal for each channel, corresponding existing power line noise, willpropagate through the system as a common mode signal and be present atthe amplifier inputs.

In some examples of high-density electrode measurement systems, the setof electrodes includes a reference electrode and a plurality of otherelectrodes. In this example, each respective pair of electrical signals,which are provided to a given differential amplifier 108, may include asignal from the reference electrode and a signal from another of theplurality of other electrodes. That is, each of the plurality ofdifferential amplifiers 108 includes a first input coupled to receive areference signal from the reference electrode and a second input coupledto receive the electrical signal (e.g., filtered signal) from one of theplurality of other electrodes. The amplifier output signal of each ofthe plurality of differential amplifiers thus provides an indication ofcommon mode signal performance between signals from the referenceelectrode and the respective other electrode. The indication of commonmode signal performance provided by each of the differential amplifiersfurther may be evaluated to determine a relative impedance of eachelectrode. For instance, high electrode impedances are either due todisconnected electrodes or non-properly connected electrodes.

As an example the channel detection logic 110 may be configured (e.g.,hardware and/or software) to implement signal processing to determine achannel integrity state for each channel. For example, the channeldetection logic 110 implements a fast Fourier transform to convert theoutput of each differential amplifier to frequency domain data having anamplitude value representing the power at different frequencies, whichcan include the frequency of the common mode signal (e.g., power linenoise). A frequency analyzer can apply a threshold to the frequencydomain data at the common mode frequency to provide the channel data 112for the plurality of input channels.

The channel data 112 thus can identify a set of one or more nodes havinglow integrity (e.g., data specifying whether channels as bad). Theoutput channel data 112 can be provided in terms of a list of nodesindexed according to input channel that can be provided to subsequentprocessing blocks so that the corresponding data for a given channel isprocessed in a particular manner or not utilized in subsequent signalprocessing and data analysis. As an example, the output channel data 112can be provided in terms of channel integrity that is considered bad,good, or can identify both bad and good channels. In some examples, alogic value (e.g., 0 or 1) can be used to specify if a channel is goodor bad. The channel integrity values for a given channel may be fixed orin some examples might change over time, such as in response to changingthe extent of contact between a given electrode and the target surface.The system thus may provide the channel data 112 without any requiringhardware modifications as well as be implemented with reduced processingtime compared to existing approaches (e.g., milliseconds versusseconds).

In some examples, spikes or other signals that may affect the FFTamplitude at the common mode frequency, are detected and removed fromthe input electrical signals for each input channel 102. For example,pacing spikes may be applied to one or more locations on the heart. Thespikes are received differently across the input channels yet stillcontribute to the FFT amplitude at the common mode frequency.Accordingly, such spikes may be detected for each input channel, forexample using a wavelet based method, and be removed from each inputsignal using spline interpolation (e.g., a piece-wise monotonic cubicspline interpolation). In this way, spikes or other signals may beexcluded from the subsequent signal processing, including the power linenoise estimation for each channel.

FIGS. 2 and 3 depict example front end hardware architectures that maybe utilized for capturing information related to common mode rejectionassociated with a pair of input channels. The examples of FIGS. 2 and 3depict front end processing for a pair of electrodes (demonstrated aselectrodes 1 and 2) 202 that receives a common input signal at A and B,demonstrated as A₁. In these examples, the input signals A₁ at A and Bcorrespond to a common mode signal, such as a power line noise signalthat may be present in any body surface measurement system. The commonmode signal thus has a frequency corresponding to the line frequency,which may vary according to location. For example, in the United States,power line noise has a frequency of about 60 Hz, whereas in Europe,power line noise has a frequency of about 50 Hz. Thus, in some examples,filter operation of the system may be switched to depending on where itis being utilized or a default value used to pass both frequencies. Thismay be done by automatically detecting the frequency of the power linesignal or in response to a user input selecting the appropriateoperating parameter according to the input power source.

In the example of FIG. 2, each of the electrodes 202 is considered toexhibit common mode behavior with being properly connected to receivethe signal A₁ at inputs A and B of the target. Thus, the signals betweenthe electrodes 202 and remaining circuitry 204 and 206, demonstrated atnodes C and D, correspond to the common mode signals. Anti-aliasingfilters 204 filter the input signals at nodes C and D to pass lowfrequency components including the line noise signals. Thus, each of thefilters 204 provides filtered signals in which the high frequencycomponents have been removed. The filtered signals at nodes E and Fcorresponding to inputs of the differential amplifier 206. Node E isconnected to a non-inverting input and node F is connected to theinverting input of amplifier 206. In the example of FIG. 2, the outputof amplifier 206 is approximately zero based on the common mode signalsat E and F being approximately equal in response to the common nodesignals at inputs A and B propagating through the system as a commonmode signal thus resulting in good common mode rejection as expected.

The example of FIG. 3 corresponds to a scenario in which one of theelectrodes 202 is not properly connected (e.g., inadequate or no contactwith the target). Specifically, in the example of FIG. 3, electrode 2 isproperly connected to the target but electrode 1 is not. Therefore, dueto its increased input impedance relative to electrode 2, the signalprovided by electrode 1 at node C has a higher amplitude than the signalat D provided by electrode 2. Based on the inadequate contact ofelectrode 1 compared to electrode 2, the common mode signal at thedifferential inputs A and B will not propagate symmetrically through thesystem to the amplifier inputs at E and F. As a result, the amplifiedoutput signal by an amplifier 206 is greater than zero corresponding topoor common mode rejection performance.

The examples of FIGS. 2 and 3 are applicable to the multi-input channelexample of FIG. 1, in which the differential amplifier is 108 eachreceives a respective different pair of filtered signals and thusprovides differential amplified outputs reflecting the common moderejection for each respective input channel pair. By comparing the powerof common mode signals at the output of each respective input channelpair and comparing such power to the power of adjacent pairs, channeldetection logic 110 can determine whether one or more bad electrodesexist. By implementing additional comparisons between electrode signalsand/or other information from the set of channels, the channel detectionlogic 110 further can detect and identify which (if any) electrodesexhibit electrode impedance values corresponding to improper connectioncriteria.

FIG. 4 depicts another example of another system 400 for determiningintegrity for a plurality of electrodes. In this example, one of theelectrodes 402 is demonstrated as a reference electrode and remainingelectrodes 404 are positioned across the body surface. Each of theelectrodes may be identical in configuration and designed to contact(e.g., directly or indirectly through an electrically conductive gel orother contact agent) the body surface for sensing electrical activity.There can be any number of electrodes 1 through N, where N is a positiveinteger. For ease of illustration, the electrodes 404 and 402 aredepicted schematically disposed on a body 406. There can be any numberand arrangement of electrodes 402 and 404 on the body surface dependingupon the function and purpose of the sensing. For example, theelectrodes can be used to sense other electrophysiological conditions,such as for use in an electrode encephalogram, electromyogram or thelike.

In the example of FIG. 4, the arrangement of the processing circuitry issimilar to as disclosed with respect to FIGS. 1-3. The system 400includes a filter 408 that is coupled between each of the electrodes402, 404 and an input of an amplifier 410. In this particular example,each of the electrodes 404 is coupled through its respective filter 408to a non-inverting input of the differential amplifier. The referenceelectrode 402 is coupled to an inverting input of the differentialamplifier via its filter 408. As disclosed herein, each of the filters408 operates as a anti aliasing filter to remove unwanted signalcomponents such as high frequencies above a known common mode signalsuch as power line interference.

For example, one or more power supplies 412 may be coupled to supplyelectrical energy to the system 400, including directly to activecircuit components, such as can include the amplifiers 410, analog todigital converters 418 and a processing device 420. For example, thepower supply may be connected to a source of AC power (e.g., a poweroutlet) and supply DC or AC power to various components in the system400. Thus, the connection of the power supply to the AC power sourceresults in power line noise on the electrical signals detected by eachof the electrodes 402 and 404. Additionally or alternatively, line noisemay also be provided from electrical devices and equipment coupled tothe system 400 (directly or indirectly) or otherwise from devicesoperating the surrounding environment, such as lights, display devicesother equipment (e.g., health monitoring equipment). While such powerline noise is filtered out via line filters to remove noise from thesensed signals for further processing, the signals of interest in theexamples disclosed herein include the line noise as a common modesignal.

The differential output from each of the amplifiers 410 are provided asanalog outputs to the ADC 418. The ADC in turn converts the analogsignal to a corresponding digital version and provides the digitalsignal to an input of the processing device 420. The processing device420 is configured (e.g., a digital signal processor, field programmablegate array, computer or other processing apparatus) to implement signalprocessing 424 and channel detection logic 430. The signal processing424 may perform signal conversion, sampling and other functions. Thechannel detection logic 430 analyzes the processed signals to determinea common mode rejection for each of the differential amplifiers, whichis supplied via the ADC blocks 418. The determined common mode rejectionfor each of the differential amplifiers is evaluated and utilized by thedetector 432 to determine channel integrity for each of the respectiveelectrodes 402 and 404.

The processing device 420 outputs channel integrity data 434. Forexample, the channel integrity data 434 can indicate whether or not eachof the electrodes 404 is connected to the target, demonstrated as thesurface of the body 406. In some examples, channel integrity data 434can also indicate whether or not the reference electrode(s) 402 isconnected to the target such as based on implementing additionalcomparisons and/or logic. In another example, manual confirmation (e.g.,via user input) may be used to specify the validity of the connection ofthe reference electrode. Additionally or alternatively, the channelintegrity data 434 may similarly specify whether the connection betweenthe electrodes 402, 404 and the body surface is unacceptable forprocessing purposes. This information can be stored as part of thechannel integrity data in a data record for each of the respectiveelectrodes 402 and 404. The channel integrity data 434 thus may bestored in memory for subsequent processing and display.

FIG. 5 depicts an example of channel detection logic 500, such as maycorrespond to channel detection logic 110 or 430. The channel detectionlogic 500 receives amplified output data 502, such as digital dataprovided by an ADC (ADC 418) corresponding to a digital version of theamplified output provided by differential amplifiers disclosed herein.For example, the amplified output data 502 represents relative commonmode signal performance or common mode rejection performance between apair of input channels. The amplified output data 502 can vary overtime, such as may include one or more time intervals.

A fast Fourier transform (FFT) 504 converts the amplified output data502 from the time domain to a corresponding frequency domainrepresentation. The frequency domain data thus represents power offrequency content that is present in signals represented in theamplified output data 502. FFT amplitude detection function 506 detectsan amplitude of power at a predetermined frequency of interest. Asmentioned, the frequency may include a frequency of the common modesignal, such as corresponding to the power line interference signal(e.g., 50 Hz or 60 Hz).

A comparator 508 compares the detected power amplitude at thepredetermined frequency with a corresponding threshold 510. Thethreshold 510 may be fixed or may be calculated based on analysis ofother common mode signals in the system. For example, channel data for aplurality of channels may be stored as channel spatial data 512. Forexample, the channel spatial data 512 may be derived from FFT amplitudedetected signals (e.g., from 506) for a group of spatially relevantelectrodes. For example, the set of electrodes arranged on the bodysurface may be grouped into two or more proper subsets of electrodes foreach of a plurality of corresponding spatial zones. Each of the spatialzones may include a subset of electrodes, and the signals for each groupof electrodes may be evaluated (e.g., over one or more time intervals)to provide common mode signal characteristics for the electrodes in eachrespective zone. As an example, the FFT amplitude detected signals foreach channel in a given zone may be processed to determine mean commonmode power (or other statistical information) for each zone channels. Athreshold calculator 514 thus may calculate a corresponding thresholdfor a given zone as a function of the mean common mode power (e.g., as apercentage or other portion of such power) for the given zone. Thus,each zone may have a corresponding zonal threshold. In some examples, aglobal threshold may be also calculated for the entire set (or aselected superset) of the electrodes. Where both global and zonalthresholds are used, for each zone, the lower of the zonal threshold andthe global threshold can be utilized as the threshold 510. Thecomparator thus may compare the threshold 510 with the FFT amplitudeprovided by FFT amplitude detection block 506 to provide correspondingchannel integrity data 516 for each of the channels.

FIGS. 6 and 7 illustrate examples of waveforms for use in demonstratingsystems and methods herein. FIG. 6 depicts an example of a graph 600that includes a plurality of waveforms 602, 604 and 606. In the exampleof FIG. 6, each of the waveforms 602, 604 and 606 corresponds to signalsfollowing a line filter in which power line interference (e.g., from thepower supply 412 in FIG. 4) has been removed. Accordingly, each of thewaveforms 602, 604 and 606 would tend to appear as acceptable inputchannels according to some existing channel integrity methods, and thusoriginate with proper electrode connections.

In the example of FIG. 7, the same signals are examined before the linefilter such as corresponding to the inputs to the differentialamplifiers disclosed herein. In this example, waveforms 702 and 704demonstrate line interference noise due to high electrode impedance andpoor common mode rejection performance. The waveform 706 demonstrates aproperly connected electrode with respect to its target surface,corresponding to a low impedance and a good connection.

FIGS. 8 and 9 depict different electrode apparatuses 800 and 900 thatinclude electrodes for sensing body surface electrical activity. In theexample of FIG. 8, the electrode apparatus 800 includes two portions 802and 804 that each includes a plurality of electrodes distributed on acontact surface. For instance, the portions 802 and 804 may be part ofgarment or patch that is configured for placement on an anterior portionof the thorax. As one example, the electrode apparatuses 800 and 900 maybe utilized in combination as a sensor array of the type as shown anddescribed in U.S. Pat. No. 9,655,561, which was filed Dec. 22, 2011, orin International patent application No. PCT/US2009/063803, which wasfiled Nov. 10, 2009, each of which applications is incorporated hereinby reference. This non-invasive sensor array corresponds to one exampleof a full complement of sensors for the patient's thorax. As anotherexample, the electrode apparatus 800, 900 can include anapplication-specific arrangement of electrodes corresponding to a singlesensing zone or multiple discrete sensing zones, such as disclosed inU.S. Pat. No. 9,549,683, which was filed Oct. 12, 2012, and isincorporated herein by reference. Additionally or alternatively, theelectrode apparatuses 800 and 900 can include arrangements of electrodesfor sensing electrical activity on other body surfaces or invasivesensors that can be inserted into the patient's body.

As shown in FIG. 8, each of the electrodes is grouped into respectivezones, demonstrated as zone 1, zone 2, and zone 3 and zone 4. Similarly,in the example of FIG. 9, the electrode apparatus 900 includes portions902 and 904 that each includes a plurality of electrodes distributed ona contact surface thereof, such as configured for placement on posteriorof the thorax. Additionally, each of the electrodes is grouped intorespective zones, demonstrated as zone 5, zone 6, and zone 7 and zone 8.

The channel integrity systems and methods disclosed herein thus mayanalyze the electrodes in each respective zone separately for purposesof determining channel integrity, such as the acceptability of electrodeconnections. Additionally, in some examples, a separate referenceelectrode may be utilized for each of the respective zones. In this way,the channel integrity systems and methods can be applied in spatiallylocalized zones to accommodate potential variations in the common modesignals that may be associated with each respective zone.

FIG. 10 is a flow diagram depicting an example of method 1000 fordetermining an acceptability or unacceptability for a plurality of inputchannels. Portions of the method 1000 may be implemented by hardware,software or a combination of hardware and software, such as disclosedherein. The method 1000 begins at 1002, in which respective inputelectrical signals are received via a plurality of input channels (e.g.,channels 102), as sensed by a set of electrodes (e.g., electrodes 202,402, 404). At 1004, the input signals are filtered (e.g., by filter 104,204 or 408). For example, the filtering (e.g., low-pass, anti-aliasing)provides corresponding filtered signals that retain power lineinterference signals as a common mode signal. The filtered signals maybe provided as inputs to respective differential amplifiers.

At 1006, respective pairs of the input signals (e.g., filtered signals)are amplified via a plurality of differential amplifiers. Thus, anamplified output signal is provided corresponding to the difference adifference between respective pairs of the input electrical signals. At1008, the amplified output signals are analyzed (e.g., by logic 110, 430and 500) to determine a relative impedance associated with eachelectrode in the set of electrodes. For example, when the amplifiedsignal is generated for an electrode pair that each has good contact,the amplified output signal will approximate zero (e.g., demonstratinggood common mode rejection performance). In examples when the amplifiedsignal is generated for an electrode pair that each does not have goodcontact, the amplified output signal will have a non-zero amplitude(e.g., demonstrating poor common mode rejection performance). The commonmode rejection performance may thus be used as a metric to determinerelative impedance for the input channels.

In some examples, the analysis at 1008 may include a signal processingmethod. The signal processing may include converting the amplifiedoutput signals of each of the plurality of differential amplifiers tofrequency domain data having an amplitude representing signal power atdifferent frequencies. This may include a range of frequencies retainedfollowing the filtering at 1004. The analysis may also include applyinga threshold to the frequency domain data at a predetermined frequencycorresponding to the common mode signal. With such signal processing,the channel data thus may be generated based on the application of thethreshold to the frequency domain data.

Additionally or alternatively, in some examples, the electrodes andassociated input channels are arranged in a plurality of spatial zonesthat each include a proper subset of the input electrical signals. Azonal threshold may be calculated for each of the plurality of spatialzones and each zonal threshold applied to the frequency domain datacorresponding to a respective zone. In this way, channel integritydetection and associated analysis may be implemented spatially for thesubsets of signals originating from each spatial zone.

At 1010, channel data (e.g., data 112, 434 and 516) is generated tospecify an acceptability or unacceptability for each of the plurality ofinput channels based on the analyzing. The channel data may be stored inmemory for subsequent processing. For example, at 1012, an output may beprovided. The output may be a visualization (e.g., graphical output)representing the acceptability of the channels, such as a channel mapsimulating the arrangement of electrodes positioned on the body surface.In other example, the output may include a map of the acquired signals,such a body surface map or a map derived by inverse reconstruction ontoa surface within the patient's body.

Additionally or alternatively, the respective input electrical signalsthat are received at 1002 may include a reference signal and a pluralityof other electrical signals. In examples where the reference signalexists (e.g., in many high-density electrode systems), each of theplurality of differential amplifiers may receive the reference signal atone input thereof and one of the plurality of other electrical signalsat another input thereof. As a result, each of the plurality ofdifferential amplifiers provides the difference signal to indicate acommon mode rejection between the reference signal and each of the otherelectrical signals.

FIG. 11 depicts an example of a system 1100 to reconstruct electricalsignals on a surface of interest (e.g., a surface envelope) based onelectrical signals measured on a different surface (e.g., body surface)that is spaced apart from the surface of interest. The system 1100includes a reconstruction engine 1102 that is programmed to reconstructthe electrical signal on the surface of interest based on geometry data1106, electrical data 1108 and channel data 1114. For example, thereconstruction engine 1102 may be programmed to implement variousmethods as part of the solution of the inverse problem, such asincluding a boundary element method (BEM) or method of fundamentalsolution (MFS). Examples of approaches that the reconstruction enginemay be programmed to implement to solve the inverse problem, includingforward and inverse computations, are disclosed in U.S. Pat. Nos.7,983,743 and 6,772,004, which are incorporated herein by reference.

The reconstruction engine 1102 can be programmed to implement an inversemethod that includes a transformation matrix calculator 1110 and aregularization method 1112. The reconstruction engine 1102 further isconfigured to impose boundary condition on the computations implementedby the transformation matrix calculator, which may include or be derivedfrom the geometry data 1106 and the electrical data 1108. The values foreach unit of the boundary condition being imposed can include fixed orvariable boundary condition parameters, such as may further vary basedon the channel data 1114.

For example, the channel data 1114 may specify one or more bad inputchannels, such as corresponding to condition where an electrode is notconnected to the target or is otherwise an unacceptable connection. Insome examples the channel data may be generated according to systems andmethods disclosed herein with respect to FIGS. 1-10. In other examples,alternative approaches may be utilized to generate the channel data 1114to provide a measure of channel integrity including to identify badchannels. An example of one such alternative approach is disclosed inU.S. Pat. No. 9,470,728, which is incorporated herein by reference.

As a further example, the geometry data 1106 can identify athree-dimensional spatial position location of the sensing electrodes(also referred to sensing nodes) in a respective coordinate system. Forexample the geometry data 1106 can include a list of nodes, and theposition for each node, such as can be produced by segmenting imagingdata that has been acquired by an appropriate imaging modality. Examplesof imaging modalities include ultrasound, computed tomography (CT), 3DRotational angiography (3DRA), magnetic resonance imaging (MRI), x-ray,positron emission tomography (PET), fluoroscopy, and the like. Suchimaging can be performed separately (e.g., before or after) themeasurements utilized to generate the electrical data 1108.Alternatively, imaging may be performed concurrently with recording theelectrical activity that is utilized to generate the patient electricaldata 1108. The geometry data 1106 can also include coordinates (e.g., inthree-dimensional space) for each of the nodes. In other examples, thegeometry data 1106 can be acquired by manual measurements betweenelectrodes or other means (e.g., a digitizer).

As another example, the geometry data 1106 can correspond to amathematical model of a torso that has been constructed based on imagedata for the patient's organ. A generic (non-patient) model can also beutilized to provide the geometry data 1106. The generic model furthermay be customized (e.g., deformed) for a given patient, such as based onpatient characteristics include size image data, health conditions orthe like. Appropriate anatomical or other landmarks, including locationsfor the electrodes can also be represented in the geometry data 1106,such as by performing segmentation of the imaging data. Theidentification of such landmarks can be done manually (e.g., by a personvia image editing software) or automatically (e.g., via image processingtechniques).

The electrical data 1108 can represent body surface electricalmeasurements acquired by an arrangement of sensing electrodes over oneor more time intervals. The body surface electrical data 1108, forexample, can include measured electrical signals (e.g., surfacepotentials) obtained from a plurality of sensing electrodes distributedacross the body surface of a patient. Similar to other examplesdisclosed herein, the distribution of electrodes can cover substantiallythe entire thorax of a patient or the sensing electrodes can bedistributed across a predetermined section of the body surface such asconfigured for detecting electrical signals predetermined as beingsufficient to detect electrical information corresponding to apredetermined region of interest for the patient's body. In otherexamples, a set of electrodes can be preconfigured to cover a selectedregion of the patient's torso for monitoring atrial electrical activityof one or both atrium of a patient's heart, such as for studying atrialfibrillation. In other examples other preconfigured sets of electrodescan be utilized according to application requirements, which can includeinvasive and non-invasive measurements. The body surface electrical data1108 thus can be stored in memory that resides in or is accessible by acomputer implementing the reconstruction engine 1102.

The transformation matrix calculator 1110 is thus programmed to computea transformation matrix, such as demonstrated at A, based on at leastone boundary condition and the geometry data 1106. The transformationmatrix A may be computed a priori or in real time during signalacquisition that provides the electrical data 1108. The transformationmatrix may include one or more submatrices, which may depend on the typeof inverse reconstruction being implemented by the reconstruction engine1102.

The reconstruction engine 1102 includes a matrix adjustment method 1116programmed to modify the transformation matrix based on the channel datato provide a modified transformation matrix. For example, the matrixadjustment method 1116 modifies the transformation matrix to ignorechannel information (e.g., values of electrical signals) captured by badelectrodes, while still retaining the spatial information (e.g.,geometry data) associated with such bad electrodes.

The regularization method 1112 is programmed to estimate thereconstructed electrical signals on the envelope based on the modifiedtransformation matrix A′ and the electrical signals from the set ofelectrodes (e.g., in the electrical data 1108). As an example, theregularization method 1112 can be programmed to implement Tikhonovregularization, such as described in the above-incorporated U.S. Pat.No. 6,772,004. Other regularization techniques may be used, includinggeneralized minimum residual (GMRes) regularization, such as disclosedin U.S. Pat. No. 7,016,719, which was filed Oct. 4, 2002, and isincorporated herein by reference. The reconstruction engine 1102 can inturn provide the reconstructed electrical signals 1104 based on theregularized matrix. The reconstructed electrical signals 1104 thusrepresent electrical signals on a cardiac envelope within the body basedon the electrical data that is acquired non-invasively using bodysurface electrodes.

By way of further example where the transformation matrix calculator1110 of the reconstruction engine 1102 uses BEM (boundary elementmethod), boundary condition data may be employed to produce a linearsystem that is constrained by each one or more boundary conditions thatis applied. The matrix adjustment method converts channel signalinformation in the transformation matrix for each bad channel, which isidentified in the channel data 1114, to unknown variables in themodified transformation matrix. The regularization method 1112 can applya regularization technique to solve the unknown values of electricalsignals on the envelope of interest from the transformation matrixcomputed by the calculator 1110. The regularization method 1112 isfurther programmed to solve for the unknown variables, which had beeninserted into the transformation matrix (by matrix adjustment method),as part of the estimation of reconstructed electrical signals on thecardiac envelope. That is, a matrix adjustment 1116 modifies thetransformation matrix by replacing sensor signal information for eachidentified bad channel with unknown values (parameters), which aresolved by the regularization method 1112.

In some examples, the reconstruction engine 1102 further can implementan inverse method that is programmed to meshlessly compute an estimateof reconstructed electrical activity using the MFS by imposing boundaryconditions to constrain certain computations, namely determiningcoefficients of the transformation matrix A. As an example, thereconstruction engine can be implemented meshlessly by imposing boundaryconditions determined from the electrical data and the geometry, such asaccording to the technique disclosed in U.S. Pat. No. 7,983,743, whichis incorporated herein by reference.

For the example where the reconstruction engine 1102 uses the MFS tosolve the inverse problem and compute the reconstructed electricalsignals 1104 on the cardiac envelope, the inverse reconstructionconstitutes a Cauchy problem for Laplace's equation:

∇² u(x)=0, x ∈ Ω

u(x)=a ₀+Σ_(i) a _(i) f(x−y _(i))

The MFS thus utilizes boundary conditions on the torso surface:

Dirichlet  boundary  condition: u(x) = u_(T)(x), x ∈ Γ_(T)Neumann  boundary  condition:${\frac{{du}(x)}{d\overset{\rightarrow}{n}} = {{c_{T}(x)} = 0}},{x \in \Gamma_{T}}$

where Ω is the 3D volume domain between the heart's epicardial surfaceand the torso surface Γ_(T)That is, the boundary conditions for that include a first boundarycondition (e.g., the Dirichlet boundary condition) that parameterizessignal channel information for the set of electrodes and a secondboundary condition (e.g., the Neumann boundary condition) thatparameterizes the spatial geometry of the set of electrodes. In thisexample, the matrix adjustment method 1116 is programmed to remove thesignal channel information from the first boundary condition (Dirichletboundary condition) for each bad channel that is identified in thechannel data 1114, while retaining the spatial geometry for the entireset of electrodes regardless of the indication of acceptability of eachof the plurality of input channels.

As an example, FIG. 13 depicts a transformation matrix 1300 that may becomputed when implementing inverse reconstruction according to MFS. Thetransformation matrix includes Dirichlet boundary conditions describingchannel information for electrical signals sensed across the bodysurface demonstrated at 1302. The transformation matrix 1300 alsoincludes Neumann boundary conditions on the torso surface correspondingto spatial geometry information associated with the electrodes on thetorso surface, demonstrated at 1304. For purposes of this example,channel information for a given row of the matrix 1300, which representsthe Dirichlet boundary condition associated with a given channel, isillustrated as being crossed out to demonstrate that it has been removed(by matrix adjustment method 1116) from the Dirichlet boundary condition1302, and thus being ignored (not utilized) in the adjustedtransformation matrix 1300. The regularization method 1112 thusestimates the value of the inverse of the transformation matrix (e.g.,Γ=A⁻¹*V_(T)) using a regularization technique such as, Tikhonov zeroorder regularization or the GMRes method. The reconstruction engine 1102in turn computes values of electrical activity on the cardiac envelopeof interest (e.g., epicardial cardiac surface potentials) node location(e.g., as disclosed in the above-incorporated U.S. Pat. No. 7,983,743).

FIG. 12 is a flow diagram depicting an example of a method 1200 forgenerating reconstructed electrical signals. The method 1200 includesstoring geometry data at 1202 and storing electrical signal data at1204. The electrical data includes electrical signals measured by aplurality of electrodes arranged for sensing body surface electricalactivity. At 1206, a transformation matrix is computed (e.g., by matrixcalculator 1110) based on at least one boundary condition and geometrydata associated with the electrodes. At 1208, the transformation matrixis modified (e.g., by matrix adjustment method) based on bad channeldata to a modified transformation matrix. The bad channel data specifiesthat a connection of one or more of the electrodes with the body surfaceis unacceptable. The matrix can be modified to ignore or remove from thematrix the contribution of signal information for each bad channel thatis identified, while retaining location information for each of theplurality of channels (including bad channel). At 1210, thereconstructed electrical signals on the cardiac envelope are estimatedbased on the modified transformation matrix and the electrical signalsfrom the plurality of electrodes. A visualization representing agraphical map of the reconstructed electrical signals on the cardiacenvelope may be generated.

In some examples, the method 1200 implements the MFS to perform theinverse reconstruction that provides the reconstructed electricalsignals. As part of the MFS, the transformation matrix includes a firstboundary condition (e.g., the Dirichlet condition) that parameterizessignal channel information for the set of electrodes and a secondboundary condition (e.g., the Neumann condition) that parameterizes thespatial geometry of the plurality of electrodes. The matrix modificationat 1208 thus may include removing the signal channel information fromthe first boundary condition for each bad channel that is identifiedwhile not changing the second boundary condition, regardless of theindication of acceptability of each of the plurality of input channels.

In another example, the method 1200 implements a boundary element methodto compute the reconstructed electrical signals on the cardiac envelope.In this example, the matrix modification at 1208 further includesconverting channel signal information in the transformation matrix foreach bad channel that is identified to an unknown parameter for acorresponding body surface signal. The estimation of reconstructedelectrical signals includes solving for each of the unknown parameters,which correspond to signal information for the bad channels.

As a further example, the bad channel data may be determined accordingto the systems and methods disclosed herein (see, e.g., FIG. 10).Briefly stated, respective input electrical signals can be sensed by theplurality of electrodes and received via corresponding input channels. Adifference between respective pairs of the input electrical signals canbe amplified (e.g., by differential amplifiers) to provide an amplifiedoutput signal corresponding to the difference. The amplified outputsignals are further analyzed to determine a relative impedanceassociated with each electrode in the set of electrodes, such as basedon the common mode rejection performance of the each amplifier. The badchannel data may be generated based on the analysis of the amplifieroutputs, such.

FIGS. 14A and 14B depict examples of graphical maps 1402 and 1404 thatcan be generated from reconstructed electrical signals for a normalsinus rhythm. The example map 1402 in FIG. 14A is generated using anexisting approach to detect the channels in which the electricalinformation for each bad channel is interpolated from its neighboringelectrodes. As demonstrated in FIG. 14A, a region of the graphical mapresults in artifacts 1408 on the resulting map 1402. By contrast, thegraphical map 1404 is generated using reconstruction method disclosedherein, in which signal information from bad channels has been ignored,and results in a graphical map that does not exhibit the artifacts.

FIGS. 15A and 15B depict examples of graphical maps 1502 and 1504 ofreconstructed electrical signals for a premature ventricularcontraction. In this example, the graphical map 1502 is generatedaccording to an existing approach in which signal information isinterpolated for bad electrodes according to the electrical informationof its neighboring electrodes. As a result, artifacts demonstrated at1506 occur in the graphical map 1502. The other graphical map 1504,however, does not exhibit such artifacts as it is reconstructed based onthis disclosure in which signal information for bad channels is ignoredwhile the geometry information for such bad channels is retained as partof the inverse reconstruction method.

FIG. 16 depicts an example of a system 1600 for generating graphicaloutputs based on electrophysiological signals measured from a patient'sbody. In some examples, the sensed electrical activity can be used togenerate one or more graphical representations (e.g., graphical maps ofelectroanatomic activity) based on the sensed electrical activity, suchas for a region of patient anatomy. The system 1600 includes a computingdevice 1602. As an example, the computing device 1602 can be implementedas a laptop computer, a desktop computer, a server, a tablet computer, aworkstation or the like. The computing device 1602 can include memory1606 for storing data and machine-readable instructions. The memory 1606can be implemented, for example, as a non-transitory computer storagemedium, such as volatile memory (e.g., random access memory),non-volatile memory (e.g., a hard disk drive, a solid-state drive, flashmemory or the like) or a combination thereof.

The computing device 1602 can also include a processing unit 1608 toaccess the memory 1606 and execute the machine-readable instructionsstored in the memory. The processing unit 1608 could be implemented, forexample, as one or more processor cores. In the present examples,although the components of the computing device 1602 are illustrated asbeing implemented on the same system, in other examples, the differentcomponents could be distributed across different systems andcommunicate, for example, over a network.

The instructions, which may be executed by the processing unit 1608include channel detection logic 1604 and/or a reconstruction engine1605. The channel detection logic 1604 may correspond to logic 110, 430as well as instructions programmed to execute portions of the method1000, as disclosed herein. The reconstruction engine 1605 may correspondto reconstruction engine 1102 as well as instructions programmed toexecute portions of the method 1200 disclosed herein. Accordingly,references may be made to earlier portions of this document foradditional information about these the channel detection logic 1604 andreconstruction engine 1605.

The system 1600 can include a measurement system 1610 to acquireelectrophysiology information for a patient 1612. In the example of FIG.16, a sensor array 1614 includes one or more electrodes that can beutilized for recording patient electrical activity. As one example, thesensor array 1614 can correspond to an arrangement of body surfaceelectrodes that are distributed over and around the patient's thorax formeasuring electrical activity associated with the patient's heart (e.g.,as part of an electrophysiology procedure). In some examples, there canbe about 200 or more sensors (e.g., about 252 sensors) in the array1614, each electrode corresponding to a node that defines a respectiveinput channel. Various examples of a non-invasive sensor array aredisclosed herein. Additionally or alternatively, the one or moreinvasive electrodes (e.g., for sensing or therapy delivery) can beinserted into the patient's body 1612.

The measurement system 1610 receives sensed electrical signals from theelectrodes in the corresponding sensor array 1614. The measurementsystem 1610 can include appropriate controls 1616 and front endcircuitry 1617 for providing corresponding electrical data 1618. Thefront end circuitry 1617 can include an arrangement of filters,amplifiers and ADCs for each respective channel, such as disclosed withrespect to FIGS. 1-4. The electrical data 1618 thus may includedigitized amplified outputs from differential amplifiers (e.g., 108, 208or 408), such as corresponding to common mode signal rejection signals,as disclosed herein. Additionally, the electrical data 1618 includessignal information that describes electrical activity sensed for each ofa plurality of input channels detected by the sensors in the sensorarray 1614, which signal information may have power line noise filteredout.

The electrical data 1618 can be stored in the memory 1606 as analog ordigital information. Appropriate time stamps and channel identifiers canbe utilized for indexing the respective electrical data 1618 tofacilitate the evaluation and analysis thereof. As an example, each ofthe sensor electrodes in the sensor array 1614 can simultaneously sensebody surface electrical activity and provide corresponding electricaldata 1618 for one or more user selected time intervals.

The device 1602 includes instructions in the memory configured toprocess the electrical data 1618 and to generate one or more outputs.The output can be stored in the memory 1606 and provided to a display1620 or other type of output device. As disclosed herein, the type ofoutput and information presented can vary depending on, for example,application requirements of the user.

As mentioned, the computing device 1602 is programmed to employ channeldetection methods 1604 to improve the accuracy in associated processingand analysis performed by the reconstruction engine 1605. The channeldetection logic 1604 can, for example, be implemented to perform anycombination of the channel analysis and detection functions and methodsdisclosed herein (see, e.g., FIGS. 1, 4, 5 and 10 and the correspondingdescription). The channel detection 1604 thus can provide channel data1622 specifying which input channels are bad (or good) based on signalprocessing on the electrical data 1618. The resulting channel integritydata 1622 provided by the detection logic 1604 can be stored in thememory 1606, separately or in conjunction with the electrical data 1618.In this way, bad channels can be tagged for further processing andanalysis.

In some examples, the channel detection 1604 can interface with agraphical user interface (GUI) 1624 stored as executable instructions inthe memory 1606. The GUI 1624 thus can provide an interactive userinterface, such that the thresholds and related parameters utilized bythe channel detection 1604 can be set in response to a user input 1625.The GUI 1624 can provide data that can be rendered as interactivegraphics on the display 1620. For example, the GUI 1624 can generate aninteractive graphical representation that differentiates between goodand bad channels (e.g., a graphical representation of the sensor array1614 differentiating graphically or otherwise between bad and goodchannels).

In the example of FIG. 16, the GUI 1624 includes a parameter selector1626 that can be employed to program parameters (e.g., thresholds,constraints, data sources and the like) utilized by the channeldetection 1604 and. In some examples, default values can be utilizedunless modified in response to a user input, such as disclosed herein.

The GUI 1624 can also include a channel selector 1628 programmed toselect and deselect channels in response to a user input. The channelselector 1628 can be employed to manually include or exclude selectedchannels, which may override bad channel information determined by thechannel detection logic 1604. For instance, the GUI 1624 can indicate(e.g., by graphical and/or textual indicators) on the display 1620 whichchannels are bad channels and/or a set of channels considered to be highintegrity (e.g., good) channels. A user can thus employ the channelselector 1628 of the GUI 1624 to include an identified bad channel thathas or exclude a good channel.

As a further example, the computing device 1602 can include a mappingsystem 1630 that is programmed to generate electroanatomical map basedon the electrical data 1618, namely based on the electrical data for thechannels.

In some examples, the mapping system 1630 includes a reconstructionengine 1605 programmed to reconstruct heart electrical activity bycombining the electrical data 1618 with geometry data 1636 through aninverse calculation. The geometry data may be generated as disclosedherein, such as including patient-specific geometry, a generic geometryinformation or any combination thereof. The reconstruction engine isprogrammed to implement an inverse method, such as disclosed herein withrespect to FIG. 11. For instance, the reconstruction engine employs atransformation matrix (e.g., computed by matrix calculator 110) and aregularization method (e.g., method 1112) to reconstruct the electricalactivity sensed by the sensor array 1614 on the patient's body onto ananatomic envelope, such as an epicardial surface, an endocardial surfaceor other envelope. The reconstruction engine 1605 further may modify thetransformation matrix based on the channel data, as disclosed herein.

The mapping system 1630 can also include a map generator 1632 that isprogrammed to generate map data representing a graphical (e.g., anelectrical or electroanatomic map) based on the electrical data 1618.The map generator 1632 can generate the map data to visualize agraphical map via the display 1620, which is spatially superimposed on agraphical representation of an anatomical structure (e.g., the bodysurface or the heart). In some examples, such as in response to the userinput 1625, the map generator 1632 can employ the reconstructedelectrical data computed via the inverse method to produce correspondingmap of electrical activity. The map can represent electrical activity ofthe patient's heart on the display 1620, such as corresponding to a mapof reconstructed electrograms (e.g., a potential map). Alternatively oradditionally, the computing device 1602 can compute other electricalcharacteristics from the reconstructed electrograms, such as anactivation map, a repolarization map, a propagation map or otherelectrical characteristic that can be computed from the measurementdata. The type of map can be set in response to the user input 1625 viathe GUI 1624.

In view of the foregoing, an automatic bad channel detection method hasbeen disclosed to improve accuracy and user experience. The approachdisclosed herein thus can enhance the user interaction and increase theease of beat-by-beat analysis. The bad channel detection methods andsystems can be implemented to identify and adjust subsequent signalprocessing methods (e.g., inverse algorithms).

As will be appreciated by those skilled in the art, portions of theinvention may be embodied as a method, data processing system, orcomputer program product. Accordingly, these portions of the presentinvention may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware. Furthermore, portions of the invention may be a computerprogram product on a computer-usable storage medium having computerreadable program code on the medium. Any suitable computer-readablemedium may be utilized including, but not limited to, static and dynamicstorage devices, hard disks, optical storage devices, and magneticstorage devices.

Certain embodiments of the invention are described herein with referenceto flowchart illustrations of methods, systems, and computer programproducts. It will be understood that blocks of the illustrations, andcombinations of blocks in the illustrations, can be implemented bycomputer-executable instructions. These computer-executable instructionsmay be provided to one or more processor of a general purpose computer,special purpose computer, or other programmable data processingapparatus (or a combination of devices and circuits) to produce amachine, such that the instructions, which execute via the processor,implement the functions specified in the block or blocks.

These computer-executable instructions may also be stored incomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory result in an article of manufacture including instructions whichimplement the function specified in the flowchart block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

What have been described above are examples. It is, of course, notpossible to describe every conceivable combination of components ormethodologies, but one of ordinary skill in the art will recognize thatmany further combinations and permutations are possible. Accordingly,the disclosure is intended to embrace all such alterations,modifications, and variations that fall within the scope of thisapplication, including the appended claims.

As used herein, the term “includes” means includes but not limited to,the term “including” means including but not limited to. The term “basedon” means based at least in part on. Additionally, where the disclosureor claims recite “a,” “an,” “a first,” or “another” element, or theequivalent thereof, it should be interpreted to include one or more thanone such element, neither requiring nor excluding two or more suchelements.

What is claimed is:
 1. A system comprising: a plurality of inputchannels configured to receive respective electrical signals from a setof electrodes; an amplifier stage that includes a plurality ofdifferential amplifiers, each of the differential amplifiers beingconfigured to provide an amplifier output signal based on a differencebetween a respective pair of the electrical signals; and channeldetection logic configured to provide channel data indicating anacceptability of each of the plurality of input channels based on ananalysis of a common mode rejection of the amplifier output signals. 2.The system of claim 1, further comprising an anti-aliasing filtercoupled between each input channel and an input of its respectivedifferential amplifier, each filter configured to provide filteredsignals having a frequency below a frequency threshold as a common modesignal to the input of the respective differential amplifiers for eachinput channel.
 3. The system of claim 2, wherein the filtered signalsprovided to the inputs of the differential amplifiers include power lineinterference signals corresponding to the common mode signal of eachdifferential amplifier.
 4. The system of claim 1, wherein the set ofelectrodes includes a reference electrode and a plurality of otherelectrodes, each respective pair of the electrical signals includes asignal from the reference electrode and one of the plurality of otherelectrodes.
 5. The system of claim 4, wherein each of the plurality ofdifferential amplifiers includes a first input coupled to receive areference signal from the reference electrode and a second input coupledto receive the electrical signal from one of the plurality of otherelectrodes, the amplifier output signal of each of the plurality ofdifferential amplifiers providing an indication of the common moderejection between the reference electrode and each respective otherelectrode.
 6. The system of claim 5, further comprising a processingunit and memory, the processing unit executes instructions stored in thememory, the instructions including the channel detection logic, whereinthe channel data indicating the acceptability for each of the pluralityof input channels is stored in the memory, and wherein the channeldetection logic further comprises: a fast Fourier transform programmedto convert the amplifier output signal of each of the plurality ofdifferential amplifiers to frequency domain data having an amplitudevalue representing a level of common mode rejection at differentfrequencies; and a frequency analyzer programmed to apply a threshold tothe frequency domain data at a predetermined frequency, corresponding tothe common mode signal of each of the plurality of differentialamplifiers, to determine the acceptability for each of the plurality ofinput channels.
 7. The system of claim 6, wherein the set of electrodesand corresponding input channels are arranged in a plurality of spatialzones, wherein the channel detection logic calculates the threshold thatthe frequency analyzer is to apply for each of the plurality of spatialzones.
 8. The system of claim 6, further comprising a low-pass filtercoupled between each input channel and an input of a respectivedifferential amplifier, each filter configured to provide a low-passfiltered signal that includes power line interference signals.
 9. Thesystem of claim 1, wherein the instructions further comprise: areconstruction engine programmed to reconstruct electrical signals on acardiac envelope based on the electrical signals from the set ofelectrodes and geometry data representing spatial geometry of the set ofelectrodes; and an output generator programmed to generate avisualization representing the reconstructed electrical signals on thecardiac envelope.
 10. The system of claim 9, wherein the reconstructionengine further comprises: a matrix calculator programmed to compute atransformation matrix based on at least one boundary condition andgeometry data associated with the electrodes; a matrix adjustment methodprogrammed to modify the transformation matrix based on theacceptability of each of the plurality of input channels to provide amodified transformation matrix; and a regularization method programmedto estimate the reconstructed electrical signals on the cardiac envelopebased on the modified transformation matrix and the electrical signalsfrom the set of electrodes.
 11. The system of claim 10, wherein thereconstruction engine implements a method of fundamental solution tocompute the reconstructed electrical signals on the cardiac envelope,wherein the transformation matrix includes a first boundary conditionthat parameterizes signal channel information for the set of electrodesand a second boundary condition that parameterizes the spatial geometryof the set of electrodes, and wherein the matrix adjustment methodremoves the signal channel information from the first boundary conditionfor each bad channel that is identified in the acceptability of each ofthe plurality of input channels while retaining the spatial geometry foreach electrode in the set of electrodes regardless of the acceptabilityof each of the plurality of input channels indicated by the channeldata.
 12. The system of claim 10, wherein the reconstruction engineimplements a boundary element method to compute the reconstructedelectrical signals on the cardiac envelope, and wherein the matrixadjustment method converts channel signal information in thetransformation matrix for each bad channel, which is identified in thechannel data of each of the plurality of input channels, to unknownvariables for the regularization method to solve as part of theestimation of reconstructed electrical signals on the cardiac envelope.13. A method, comprising: receiving, via a plurality of input channels,respective input electrical signals sensed by a set of electrodes;amplifying, via a plurality of differential amplifiers, a differencebetween respective pairs of the input electrical signals and providingan amplified output signal corresponding to the difference; analyzingthe amplified output signals to determine a relative impedanceassociated with each electrode in the set of electrodes; and generatingchannel data to specify an acceptability or unacceptability for each ofthe plurality of input channels based on the analyzing.
 14. The methodof claim 13, further comprising filtering the input electrical signalsto provide corresponding filtered signals to the inputs of eachrespective differential amplifier.
 15. The method of claim 14, whereinthe filtered signals include power line interference signals as a commonmode signal.
 16. The method of claim 15, wherein the respective inputelectrical signals include a reference signal and a plurality of otherelectrical signals, and wherein each of the plurality of differentialamplifiers receive the reference signal at one input thereof and one ofthe plurality of other electrical signals at another input thereof, eachof the plurality of differential amplifiers providing the amplifiedoutput signal to indicate a common mode rejection between the referencesignal and each of the other electrical signals.
 17. The method of claim16, wherein analyzing the amplified output signals further comprises:converting the amplified output signals of each of the plurality ofdifferential amplifiers to frequency domain data having an amplituderepresenting signal components at different frequencies; and applying athreshold to the frequency domain data at a predetermined frequencycorresponding to the common mode signal, wherein the channel data isgenerated based on the application of the threshold to the frequencydomain data.
 18. The method of claim 17, wherein the set of electrodesand associated input channels are arranged in a plurality of spatialzones that each include a proper subset of the input electrical signals,the method further comprising: calculating a zonal threshold for each ofthe plurality of spatial zones; and applying each zonal threshold for arespective zone to the frequency domain data associated with therespective zone to implement analysis spatially for signals originatingfrom each spatial zone.
 19. The method of claim 13, further comprising:reconstructing electrical signals on a cardiac envelope based on theelectrical signals from the set of electrodes and geometry datarepresenting geometry of the set of electrodes relative to anatomy; andgenerating a graphical output that displays the reconstructed electricalsignals on the cardiac envelope.
 20. The method of claim 19, whereinreconstructing electrical signals further comprises: calculating atransformation matrix based on at least one boundary condition and thegeometry data; adjusting the transformation matrix based on the channeldata to provide a modified transformation matrix; and estimating thereconstructed electrical signals on the cardiac envelope based on themodified transformation matrix and the input electrical signals.
 21. Themethod of claim 20, wherein the transformation matrix includes a firstboundary condition that parameterizes signal information for the set ofelectrodes and a second boundary condition that parameterizes thegeometry of the set of electrodes, and wherein adjusting thetransformation matrix further comprises removing the signal informationfrom the first boundary condition for each channel that is specified asunacceptable in the channel data while retaining the spatial geometryfor each of the electrodes regardless of the acceptability orunacceptability thereof specified by the channel data.
 22. The method ofclaim 20, wherein adjusting the transformation matrix further comprises:converting channel signal information in the transformation matrix foreach for each unacceptable channel that is specified in the channel datato unknown variables in the modified transformation matrix, wherein theunknown variables are solved as part of the estimation of reconstructedelectrical signals on the cardiac envelope.
 23. A system comprising: aplurality of electrodes configured to sense electrical signals across abody surface of a patient; a processor that executes machine readableinstructions stored in one or more non-transitory media, theinstructions configured to at least: compute a transformation matrixbased on at least one boundary condition and geometry data associatedwith the plurality of electrodes; modify the transformation matrix basedon bad channel data specifying that one or more of a plurality of inputchannels, which receive electrical signals from the plurality ofelectrodes, are unacceptable while retaining geometry information foreach of the plurality of electrodes; and estimate reconstructedelectrical signals on a cardiac envelope based on the modifiedtransformation matrix and the electrical signals from the plurality ofelectrodes.
 24. The system of claim 23, wherein the instructions areprogrammed to implement a method of fundamental solution to compute thereconstructed electrical signals on the cardiac envelope, wherein thetransformation matrix includes a first boundary condition thatparameterizes signal channel information for the set of electrodes and asecond boundary condition that parameterizes the spatial geometry of theset of electrodes, and wherein the instructions are further programmedto remove the signal channel information from the first boundarycondition for each bad channel that is identified in the bad channeldata while retaining the spatial geometry for each of the electrodes inthe second boundary condition regardless of the bad channel data. 25.The system of claim 23, wherein the instructions are programmed toimplement a boundary element method to compute the reconstructedelectrical signals on the cardiac envelope, and wherein the instructionsare further programmed to convert channel signal information in thetransformation matrix for each bad channel, which is identified in thebad channel data, to unknown variables solved as part of the estimationof reconstructed electrical signals on the cardiac envelope.
 26. Thesystem of claim 23 further comprising a display that generates avisualization representing a graphical map of the reconstructedelectrical signals on the cardiac envelope.
 27. The system of claim 23,further comprising: a plurality of input channels configured to receiverespective electrical signals from the plurality of electrodes; and anamplifier stage that includes a plurality of differential amplifiers,each of the differential amplifiers being configured to provide anamplifier output signal based on a difference between a respective pairof the electrical signals, wherein the instructions are furtherconfigured to provide the bad channel data based on a relative impedanceof the electrodes determined from evaluating the amplifier outputsignals.
 28. A method, comprising: storing geometry data and electricalsignal data associated with a plurality of electrodes arranged forsensing body surface electrical signals; computing a transformationmatrix based on at least one boundary condition and geometry dataassociated with the electrodes; modifying the transformation matrixbased on bad channel data specifying that a connection of one or more ofa plurality of electrodes with the body surface is unacceptable whileretaining location information for each of the plurality of channels andproviding a modified transformation matrix; and estimating thereconstructed electrical signals on the cardiac envelope based on themodified transformation matrix and the electrical signals from theplurality of electrodes.
 29. The method of claim 28, wherein thetransformation matrix includes a first boundary condition thatparameterizes signal channel information for the set of electrodes and asecond boundary condition that parameterizes the spatial geometry of theplurality of electrodes, and wherein modifying the transformation matrixfurther comprises removing the signal channel information from the firstboundary condition for each bad channel that is specified in the badchannel data while unchanging the second boundary condition regardlessof the acceptability of each of the plurality of input channels.
 30. Themethod of claim 28, wherein a boundary element method is used to computethe reconstructed electrical signals on the cardiac envelope, andwherein the method further comprises converting channel signalinformation in the transformation matrix for each bad channel that isidentified to an unknown parameter for a corresponding body surfacesignal, wherein estimating the reconstructed electrical signals furtherincludes solving for each unknown parameter.
 31. The method of claim 28,further comprising: receiving, via a plurality of input channels,respective input electrical signals sensed by the plurality ofelectrodes; amplifying, via a plurality of differential amplifiers, adifference between respective pairs of the input electrical signals andproviding an amplified output signal corresponding to the difference;analyzing the amplified output signals to determine a relative impedanceassociated with each electrode in the set of electrodes; and generatingthe bad channel data based on the analyzing.